




'''simple docstring'''


import argparse
import shutil
from pathlib import Path

from tqdm import tqdm

from transformers import AutoTokenizer


def     SCREAMING_SNAKE_CASE							(   a_						:				Optional[int]					,       a_						:				List[Any]					,       a_						:				List[Any]					,       a_						:				Optional[Any]=1024      ):
  __a						,							__a								=       [], []

  __a								=       list(zip(a_					,       a_      )      )
  __a						,							__a								=       sorted_examples[0]

  def is_too_big(a_						:				Union[str, Any]      ):
    return tok(a_					,       return_tensors='pt'      ).input_ids.shape[1] > max_tokens

  for src, tgt in tqdm(sorted_examples[1:]      ):
    __a								=       new_src + ' ' + src
    __a								=       new_tgt + ' ' + tgt
    if is_too_big(a_      ) or is_too_big(a_      ):  # cant fit, finalize example
      finished_src.append(a_      )
      finished_tgt.append(a_      )
      __a						,							__a								=       src, tgt
    else:  # can fit, keep adding
      __a						,							__a								=       cand_src, cand_tgt

    # cleanup
  if new_src:
    assert new_tgt
    finished_src.append(a_      )
    finished_tgt.append(a_      )
  return finished_src, finished_tgt


def     SCREAMING_SNAKE_CASE							(   a_						:				int					,       a_						:				Path					,       a_						:				Tuple					,       a_						:				int      ):
  __a								=       Path(a_      )
  save_path.mkdir(exist_ok=a_      )
  for split in ["train"]:
    __a						,							__a								=       data_dir / f"{split}.source", data_dir / f"{split}.target"
    __a								=       [x.rstrip() for x in Path(a_      ).open().readlines()]
    __a								=       [x.rstrip() for x in Path(a_      ).open().readlines()]
    __a						,							__a								=       pack_examples(a_					,       a_					,       a_					,       a_      )
    print(f"packed {split} split from {len(a_      )} examples -> {len(a_      )}."      )
    Path(save_path / f"{split}.source"      ).open('w'      ).write('\n'.join(a_      )      )
    Path(save_path / f"{split}.target"      ).open('w'      ).write('\n'.join(a_      )      )
  for split in ["val", "test"]:
    __a						,							__a								=       data_dir / f"{split}.source", data_dir / f"{split}.target"
    shutil.copyfile(a_					,       save_path / f"{split}.source"      )
    shutil.copyfile(a_					,       save_path / f"{split}.target"      )


def     SCREAMING_SNAKE_CASE							(   ):
  __a								=       argparse.ArgumentParser()
  parser.add_argument('--tok_name'					,       type=a_					,       help='like facebook/bart-large-cnn,t5-base, etc.'      )
  parser.add_argument('--max_seq_len'					,       type=a_					,       default=128      )
  parser.add_argument('--data_dir'					,       type=a_      )
  parser.add_argument('--save_path'					,       type=a_      )
  __a								=       parser.parse_args()
  __a								=       AutoTokenizer.from_pretrained(args.tok_name      )
  return pack_data_dir(a_					,       Path(args.data_dir      )					,       args.max_seq_len					,       args.save_path      )


if __name__ == "__main__":
  packer_cli()









'''simple docstring'''


from __future__ import annotations

import csv

import requests
from bsa import BeautifulSoup


def     SCREAMING_SNAKE_CASE							(   a_						:				str = ""      ):
  __a								=       url or 'https://www.imdb.com/chart/top/?ref_=nv_mv_250'
  __a								=       BeautifulSoup(requests.get(a_      ).text					,       'html.parser'      )
  __a								=       soup.find_all('td'					,       attrs='titleColumn'      )
  __a								=       soup.find_all('td'					,       class_='ratingColumn imdbRating'      )
  return {
      title.a.text: float(rating.strong.text      )
      for title, rating in zip(a_					,       a_      )
  }


def     SCREAMING_SNAKE_CASE							(   a_						:				str = "IMDb_Top_250_Movies.csv"      ):
  __a								=       get_imdb_top_aaa_movies()
  with open(a_					,       'w'					,       newline=''      ) as out_file:
    __a								=       csv.writer(a_      )
    writer.writerow(['Movie title', 'IMDb rating']      )
    for title, rating in movies.items():
      writer.writerow([title, rating]      )


if __name__ == "__main__":
  write_movies()



